CORDIS - EU research results

E2-CREATE: Encoding Embodied CreativityVisual arts, performing arts, film, design

Project description

Translating hidden processes of elusive motion art

Dance is one of the most creative forms of art. Through choreography, it embodies emotions and thoughts through precisely practised motions. This process has long been considered very elusive to be studied and documented. However, this is changing in our digital world that is enabling the recordings and studies of choreographic processes. The EU-funded E2-CREATE project will conduct interdisciplinary research combining computational arts and artistic skills in dance with the support of computer vision, machine learning and generative computer art. This will enable a new level of knowledgeable transfer at the crossroads of art and science to encode embodied creativity in the performing arts.


Choreography is an art form well-known for combining rigorous physical and mental training with the highest degrees of creativity. However, as dance is the most ephemeral of art forms, this extraordinary embodied creativity has been in danger of disappearing without leaving a significant trace. That was until several leading dance artists began over a decade ago to experiment with digital technology as a means to document their unique approaches to choreography. The result today is an impressive accumulation of interdisciplinary research showing how embodied creativity in dance can be systematically studied and documented, how computer-aided design can effectively communicate the outcomes and how digitised recordings can be processed computationally to reveal new information about choreographic principles, processes and methods. Drawing on these developments in dance digitisation and recent progress in computational arts, the Fellowship will focus on fusing artistic skills in dance with artistic skills in computing. The goal is to achieve a new level of sophisticated creative transfer at the intersection of the two fields by taking advantage of significant advances in the fields of computer vision and machine learning combined with the high-level of expertise the Fellow brings from the field of generative computer art. Generative computer art techniques have until now not been integrated fully into the dance digitisation process, but they have extraordinary potential in combination with machine learning to expand the capability of computational systems to learn from and model existing artistic approaches. The Fellow’s extensive experience of working at the intersection between dance technology and computer art and science means he is extremely well placed to facilitate and achieve this encoding of embodied creativity with lasting impact for mixed machine-human collaboration and interdisciplinary art and science research.


Net EU contribution
€ 212 933,76
CV1 5FB Coventry
United Kingdom

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West Midlands (England) West Midlands Coventry
Activity type
Higher or Secondary Education Establishments
Total cost
€ 212 933,76